Overview

Dataset statistics

Number of variables12
Number of observations160
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.2 KiB
Average record size in memory104.0 B

Variable types

Text2
Numeric9
Categorical1

Alerts

All Grades is highly overall correlated with Total Students and 4 other fieldsHigh correlation
Total Students is highly overall correlated with All Grades and 3 other fieldsHigh correlation
AfriAmerican-Black-% is highly overall correlated with All Grades and 1 other fieldsHigh correlation
Secondary is highly overall correlated with PrimaryHigh correlation
High is highly overall correlated with All Grades and 4 other fieldsHigh correlation
Tests Taken is highly overall correlated with All Grades and 5 other fieldsHigh correlation
% Score 1-2 is highly overall correlated with Tests Taken and 1 other fieldsHigh correlation
% Score 3-5 is highly overall correlated with Tests Taken and 1 other fieldsHigh correlation
Primary is highly overall correlated with All Grades and 4 other fieldsHigh correlation
Primary is highly imbalanced (90.6%)Imbalance
District Name_x has unique valuesUnique
District Code has unique valuesUnique
District Name_y has unique valuesUnique
All Grades has 25 (15.6%) zerosZeros
AfriAmerican-Black-% has 25 (15.6%) zerosZeros
Secondary has 130 (81.2%) zerosZeros
High has 25 (15.6%) zerosZeros
% Score 1-2 has 141 (88.1%) zerosZeros
% Score 3-5 has 145 (90.6%) zerosZeros

Reproduction

Analysis started2023-07-02 22:20:20.983118
Analysis finished2023-07-02 22:20:26.611254
Duration5.63 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

District Name_x
Text

UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:26.762619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length77
Median length57
Mean length17.53125
Min length4

Characters and Unicode

Total characters2805
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)100.0%

Sample

1st rowAbington
2nd rowAcademy Of the Pacific Rim Charter Public (District)
3rd rowAdvanced Math and Science Academy Charter (District)
4th rowAmherst-Pelham
5th rowAndover
ValueCountFrequency (%)
district 27
 
7.9%
charter 24
 
7.0%
regional 15
 
4.4%
vocational 12
 
3.5%
school 12
 
3.5%
technical 11
 
3.2%
academy 9
 
2.6%
public 6
 
1.8%
of 6
 
1.8%
science 4
 
1.2%
Other values (182) 215
63.0%
2023-07-02T18:20:27.016333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 230
 
8.2%
o 210
 
7.5%
t 206
 
7.3%
a 203
 
7.2%
r 198
 
7.1%
181
 
6.5%
i 176
 
6.3%
n 159
 
5.7%
l 151
 
5.4%
c 120
 
4.3%
Other values (43) 971
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2199
78.4%
Uppercase Letter 357
 
12.7%
Space Separator 181
 
6.5%
Close Punctuation 26
 
0.9%
Open Punctuation 26
 
0.9%
Dash Punctuation 14
 
0.5%
Other Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 230
10.5%
o 210
9.5%
t 206
9.4%
a 203
9.2%
r 198
9.0%
i 176
 
8.0%
n 159
 
7.2%
l 151
 
6.9%
c 120
 
5.5%
h 105
 
4.8%
Other values (14) 441
20.1%
Uppercase Letter
ValueCountFrequency (%)
C 44
12.3%
S 40
11.2%
D 33
 
9.2%
R 25
 
7.0%
A 24
 
6.7%
M 22
 
6.2%
B 22
 
6.2%
P 21
 
5.9%
W 19
 
5.3%
T 18
 
5.0%
Other values (13) 89
24.9%
Other Punctuation
ValueCountFrequency (%)
' 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
181
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2556
91.1%
Common 249
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 230
 
9.0%
o 210
 
8.2%
t 206
 
8.1%
a 203
 
7.9%
r 198
 
7.7%
i 176
 
6.9%
n 159
 
6.2%
l 151
 
5.9%
c 120
 
4.7%
h 105
 
4.1%
Other values (37) 798
31.2%
Common
ValueCountFrequency (%)
181
72.7%
) 26
 
10.4%
( 26
 
10.4%
- 14
 
5.6%
' 1
 
0.4%
. 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 230
 
8.2%
o 210
 
7.5%
t 206
 
7.3%
a 203
 
7.2%
r 198
 
7.1%
181
 
6.5%
i 176
 
6.3%
n 159
 
5.7%
l 151
 
5.4%
c 120
 
4.3%
Other values (43) 971
34.6%

District Code
Real number (ℝ)

UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3724937.5
Minimum0
Maximum35060000
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:27.111800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile199000
Q11242500
median2725000
Q34872500
95-th percentile8311500
Maximum35060000
Range35060000
Interquartile range (IQR)3630000

Descriptive statistics

Standard deviation4387719
Coefficient of variation (CV)1.1779309
Kurtosis31.111705
Mean3724937.5
Median Absolute Deviation (MAD)1745000
Skewness4.6844827
Sum5.9599 × 108
Variance1.9252078 × 1013
MonotonicityNot monotonic
2023-07-02T18:20:27.184489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 1
 
0.6%
4120000 1
 
0.6%
2070000 1
 
0.6%
2110000 1
 
0.6%
2120000 1
 
0.6%
7300000 1
 
0.6%
8530000 1
 
0.6%
2200000 1
 
0.6%
2260000 1
 
0.6%
2290000 1
 
0.6%
Other values (150) 150
93.8%
ValueCountFrequency (%)
0 1
0.6%
10000 1
0.6%
90000 1
0.6%
100000 1
0.6%
140000 1
0.6%
160000 1
0.6%
170000 1
0.6%
180000 1
0.6%
200000 1
0.6%
250000 1
0.6%
ValueCountFrequency (%)
35060000 1
0.6%
35030000 1
0.6%
9100000 1
0.6%
8850000 1
0.6%
8760000 1
0.6%
8730000 1
0.6%
8720000 1
0.6%
8530000 1
0.6%
8300000 1
0.6%
8280000 1
0.6%

All Grades
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.60625
Minimum0
Maximum2843
Zeros25
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:27.258100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q314.25
95-th percentile60.05
Maximum2843
Range2843
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation227.52975
Coefficient of variation (CV)6.7704594
Kurtosis148.83378
Mean33.60625
Median Absolute Deviation (MAD)4
Skewness12.03277
Sum5377
Variance51769.787
MonotonicityNot monotonic
2023-07-02T18:20:27.322163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 25
15.6%
2 21
 
13.1%
1 16
 
10.0%
4 11
 
6.9%
6 8
 
5.0%
9 6
 
3.8%
3 5
 
3.1%
8 5
 
3.1%
7 5
 
3.1%
5 4
 
2.5%
Other values (32) 54
33.8%
ValueCountFrequency (%)
0 25
15.6%
1 16
10.0%
2 21
13.1%
3 5
 
3.1%
4 11
6.9%
5 4
 
2.5%
6 8
 
5.0%
7 5
 
3.1%
8 5
 
3.1%
9 6
 
3.8%
ValueCountFrequency (%)
2843 1
 
0.6%
464 1
 
0.6%
157 1
 
0.6%
141 1
 
0.6%
120 1
 
0.6%
105 1
 
0.6%
69 1
 
0.6%
61 1
 
0.6%
60 1
 
0.6%
52 3
1.9%

Total Students
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean959.8375
Minimum10
Maximum82946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:27.395516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20.95
Q158
median160
Q3368.5
95-th percentile1288.9
Maximum82946
Range82936
Interquartile range (IQR)310.5

Descriptive statistics

Standard deviation6651.0269
Coefficient of variation (CV)6.9293259
Kurtosis147.82087
Mean959.8375
Median Absolute Deviation (MAD)115.5
Skewness11.975573
Sum153574
Variance44236159
MonotonicityNot monotonic
2023-07-02T18:20:27.467014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 5
 
3.1%
21 3
 
1.9%
104 3
 
1.9%
72 3
 
1.9%
58 2
 
1.2%
57 2
 
1.2%
136 2
 
1.2%
252 2
 
1.2%
14 2
 
1.2%
23 2
 
1.2%
Other values (127) 134
83.8%
ValueCountFrequency (%)
10 1
 
0.6%
13 1
 
0.6%
14 2
1.2%
16 1
 
0.6%
19 1
 
0.6%
20 2
1.2%
21 3
1.9%
23 2
1.2%
25 1
 
0.6%
26 1
 
0.6%
ValueCountFrequency (%)
82946 1
0.6%
12761 1
0.6%
8936 1
0.6%
4024 1
0.6%
3841 1
0.6%
1695 1
0.6%
1622 1
0.6%
1401 1
0.6%
1283 1
0.6%
1199 1
0.6%

AfriAmerican-Black-%
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2813751
Minimum0
Maximum47.619048
Zeros25
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:27.539134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0725909
median2.9948131
Q36.4059391
95-th percentile18.937885
Maximum47.619048
Range47.619048
Interquartile range (IQR)5.3333482

Descriptive statistics

Standard deviation6.898282
Coefficient of variation (CV)1.3061526
Kurtosis10.724068
Mean5.2813751
Median Absolute Deviation (MAD)2.1750859
Skewness2.7653427
Sum845.02001
Variance47.586295
MonotonicityNot monotonic
2023-07-02T18:20:27.613770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
15.6%
3.703703704 3
 
1.9%
10 2
 
1.2%
2.127659574 2
 
1.2%
1.388888889 2
 
1.2%
13.79310345 2
 
1.2%
5.357142857 2
 
1.2%
1.941747573 2
 
1.2%
4.8 1
 
0.6%
1.398601399 1
 
0.6%
Other values (118) 118
73.8%
ValueCountFrequency (%)
0 25
15.6%
0.380952381 1
 
0.6%
0.4016064257 1
 
0.6%
0.4170141785 1
 
0.6%
0.6211180124 1
 
0.6%
0.7518796992 1
 
0.6%
0.8130081301 1
 
0.6%
0.826446281 1
 
0.6%
0.8333333333 1
 
0.6%
0.8438818565 1
 
0.6%
ValueCountFrequency (%)
47.61904762 1
0.6%
35 1
0.6%
26.85185185 1
0.6%
25.67567568 1
0.6%
22.22222222 1
0.6%
20 1
0.6%
19.80676329 1
0.6%
19.29824561 1
0.6%
18.91891892 1
0.6%
17.85714286 1
0.6%

Primary
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0.0
156 
13.0
 
1
6.0
 
1
4.0
 
1
33.0
 
1

Length

Max length4
Median length3
Mean length3.0125
Min length3

Characters and Unicode

Total characters482
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)2.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156
97.5%
13.0 1
 
0.6%
6.0 1
 
0.6%
4.0 1
 
0.6%
33.0 1
 
0.6%

Length

2023-07-02T18:20:27.680110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-02T18:20:27.753093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156
97.5%
13.0 1
 
0.6%
6.0 1
 
0.6%
4.0 1
 
0.6%
33.0 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 316
65.6%
. 160
33.2%
3 3
 
0.6%
1 1
 
0.2%
6 1
 
0.2%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 322
66.8%
Other Punctuation 160
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 316
98.1%
3 3
 
0.9%
1 1
 
0.3%
6 1
 
0.3%
4 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 482
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 316
65.6%
. 160
33.2%
3 3
 
0.6%
1 1
 
0.2%
6 1
 
0.2%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 316
65.6%
. 160
33.2%
3 3
 
0.6%
1 1
 
0.2%
6 1
 
0.2%
4 1
 
0.2%

Secondary
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.86875
Minimum0
Maximum527
Zeros130
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:27.806135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16.1
Maximum527
Range527
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42.600989
Coefficient of variation (CV)7.2589545
Kurtosis143.34406
Mean5.86875
Median Absolute Deviation (MAD)0
Skewness11.729272
Sum939
Variance1814.8443
MonotonicityNot monotonic
2023-07-02T18:20:27.863602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 130
81.2%
1 5
 
3.1%
3 4
 
2.5%
15 3
 
1.9%
6 3
 
1.9%
13 2
 
1.2%
29 2
 
1.2%
16 1
 
0.6%
45 1
 
0.6%
5 1
 
0.6%
Other values (8) 8
 
5.0%
ValueCountFrequency (%)
0 130
81.2%
1 5
 
3.1%
2 1
 
0.6%
3 4
 
2.5%
5 1
 
0.6%
6 3
 
1.9%
7 1
 
0.6%
11 1
 
0.6%
13 2
 
1.2%
15 3
 
1.9%
ValueCountFrequency (%)
527 1
 
0.6%
98 1
 
0.6%
45 1
 
0.6%
29 2
1.2%
27 1
 
0.6%
19 1
 
0.6%
18 1
 
0.6%
16 1
 
0.6%
15 3
1.9%
13 2
1.2%

High
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.3875
Minimum0
Maximum2283
Zeros25
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:28.037135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310.25
95-th percentile52.4
Maximum2283
Range2283
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation184.05987
Coefficient of variation (CV)6.7205796
Kurtosis144.44984
Mean27.3875
Median Absolute Deviation (MAD)3
Skewness11.806348
Sum4382
Variance33878.038
MonotonicityNot monotonic
2023-07-02T18:20:28.102032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 25
15.6%
1 24
15.0%
2 19
11.9%
3 10
 
6.2%
6 10
 
6.2%
4 9
 
5.6%
8 7
 
4.4%
5 6
 
3.8%
7 5
 
3.1%
9 4
 
2.5%
Other values (26) 41
25.6%
ValueCountFrequency (%)
0 25
15.6%
1 24
15.0%
2 19
11.9%
3 10
 
6.2%
4 9
 
5.6%
5 6
 
3.8%
6 10
 
6.2%
7 5
 
3.1%
8 7
 
4.4%
9 4
 
2.5%
ValueCountFrequency (%)
2283 1
 
0.6%
461 1
 
0.6%
157 1
 
0.6%
126 1
 
0.6%
75 1
 
0.6%
69 1
 
0.6%
61 1
 
0.6%
60 1
 
0.6%
52 3
1.9%
41 1
 
0.6%

District Name_y
Text

UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:28.254112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length77
Median length57
Mean length17.53125
Min length4

Characters and Unicode

Total characters2805
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)100.0%

Sample

1st rowAbington
2nd rowAcademy Of the Pacific Rim Charter Public (District)
3rd rowAdvanced Math and Science Academy Charter (District)
4th rowAmherst-Pelham
5th rowAndover
ValueCountFrequency (%)
district 27
 
7.9%
charter 24
 
7.0%
regional 15
 
4.4%
vocational 12
 
3.5%
school 12
 
3.5%
technical 11
 
3.2%
academy 9
 
2.6%
public 6
 
1.8%
of 6
 
1.8%
science 4
 
1.2%
Other values (182) 215
63.0%
2023-07-02T18:20:28.506180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 230
 
8.2%
o 210
 
7.5%
t 206
 
7.3%
a 203
 
7.2%
r 198
 
7.1%
181
 
6.5%
i 176
 
6.3%
n 159
 
5.7%
l 151
 
5.4%
c 120
 
4.3%
Other values (43) 971
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2199
78.4%
Uppercase Letter 357
 
12.7%
Space Separator 181
 
6.5%
Close Punctuation 26
 
0.9%
Open Punctuation 26
 
0.9%
Dash Punctuation 14
 
0.5%
Other Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 230
10.5%
o 210
9.5%
t 206
9.4%
a 203
9.2%
r 198
9.0%
i 176
 
8.0%
n 159
 
7.2%
l 151
 
6.9%
c 120
 
5.5%
h 105
 
4.8%
Other values (14) 441
20.1%
Uppercase Letter
ValueCountFrequency (%)
C 44
12.3%
S 40
11.2%
D 33
 
9.2%
R 25
 
7.0%
A 24
 
6.7%
M 22
 
6.2%
B 22
 
6.2%
P 21
 
5.9%
W 19
 
5.3%
T 18
 
5.0%
Other values (13) 89
24.9%
Other Punctuation
ValueCountFrequency (%)
' 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
181
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2556
91.1%
Common 249
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 230
 
9.0%
o 210
 
8.2%
t 206
 
8.1%
a 203
 
7.9%
r 198
 
7.7%
i 176
 
6.9%
n 159
 
6.2%
l 151
 
5.9%
c 120
 
4.7%
h 105
 
4.1%
Other values (37) 798
31.2%
Common
ValueCountFrequency (%)
181
72.7%
) 26
 
10.4%
( 26
 
10.4%
- 14
 
5.6%
' 1
 
0.4%
. 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 230
 
8.2%
o 210
 
7.5%
t 206
 
7.3%
a 203
 
7.2%
r 198
 
7.1%
181
 
6.5%
i 176
 
6.3%
n 159
 
5.7%
l 151
 
5.4%
c 120
 
4.3%
Other values (43) 971
34.6%

Tests Taken
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.25
Minimum1
Maximum1140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:28.589022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile25.4
Maximum1140
Range1139
Interquartile range (IQR)4

Descriptive statistics

Standard deviation92.490829
Coefficient of variation (CV)6.4905845
Kurtosis140.59757
Mean14.25
Median Absolute Deviation (MAD)1
Skewness11.604701
Sum2280
Variance8554.5535
MonotonicityNot monotonic
2023-07-02T18:20:28.646129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 57
35.6%
2 28
17.5%
3 16
 
10.0%
4 16
 
10.0%
5 7
 
4.4%
8 6
 
3.8%
6 5
 
3.1%
13 4
 
2.5%
10 3
 
1.9%
9 3
 
1.9%
Other values (13) 15
 
9.4%
ValueCountFrequency (%)
1 57
35.6%
2 28
17.5%
3 16
 
10.0%
4 16
 
10.0%
5 7
 
4.4%
6 5
 
3.1%
7 3
 
1.9%
8 6
 
3.8%
9 3
 
1.9%
10 3
 
1.9%
ValueCountFrequency (%)
1140 1
0.6%
257 1
0.6%
94 1
0.6%
72 1
0.6%
71 1
0.6%
40 1
0.6%
36 1
0.6%
33 1
0.6%
25 1
0.6%
18 1
0.6%

% Score 1-2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.230625
Minimum0
Maximum100
Zeros141
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:28.703439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile82.54
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.015796
Coefficient of variation (CV)2.8184219
Kurtosis5.3481007
Mean9.230625
Median Absolute Deviation (MAD)0
Skewness2.6265182
Sum1476.9
Variance676.82163
MonotonicityNot monotonic
2023-07-02T18:20:28.759039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 141
88.1%
100 4
 
2.5%
50 1
 
0.6%
70.8 1
 
0.6%
83.3 1
 
0.6%
84.6 1
 
0.6%
98.6 1
 
0.6%
90 1
 
0.6%
82.5 1
 
0.6%
80 1
 
0.6%
Other values (7) 7
 
4.4%
ValueCountFrequency (%)
0 141
88.1%
38.9 1
 
0.6%
48 1
 
0.6%
50 1
 
0.6%
60 1
 
0.6%
63.8 1
 
0.6%
70.6 1
 
0.6%
70.8 1
 
0.6%
77 1
 
0.6%
78.8 1
 
0.6%
ValueCountFrequency (%)
100 4
2.5%
98.6 1
 
0.6%
90 1
 
0.6%
84.6 1
 
0.6%
83.3 1
 
0.6%
82.5 1
 
0.6%
80 1
 
0.6%
78.8 1
 
0.6%
77 1
 
0.6%
70.8 1
 
0.6%

% Score 3-5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.644375
Minimum0
Maximum61.1
Zeros145
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-07-02T18:20:28.814126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.29
Maximum61.1
Range61.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.6310932
Coefficient of variation (CV)3.6421057
Kurtosis17.718218
Mean2.644375
Median Absolute Deviation (MAD)0
Skewness4.1339272
Sum423.1
Variance92.757956
MonotonicityNot monotonic
2023-07-02T18:20:28.867718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 145
90.6%
40 1
 
0.6%
23 1
 
0.6%
61.1 1
 
0.6%
21.2 1
 
0.6%
36.2 1
 
0.6%
52 1
 
0.6%
20 1
 
0.6%
50 1
 
0.6%
17.5 1
 
0.6%
Other values (6) 6
 
3.8%
ValueCountFrequency (%)
0 145
90.6%
1.4 1
 
0.6%
10 1
 
0.6%
15.4 1
 
0.6%
16.7 1
 
0.6%
17.5 1
 
0.6%
20 1
 
0.6%
21.2 1
 
0.6%
23 1
 
0.6%
29.2 1
 
0.6%
ValueCountFrequency (%)
61.1 1
0.6%
52 1
0.6%
50 1
0.6%
40 1
0.6%
36.2 1
0.6%
29.4 1
0.6%
29.2 1
0.6%
23 1
0.6%
21.2 1
0.6%
20 1
0.6%

Interactions

2023-07-02T18:20:25.868133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.484877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.039137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.547028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.134758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.639382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.165722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.737011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.354697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.954055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.561670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.101903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.607626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.196029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.705743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.294435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.799873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.417960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:26.010493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.621630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.157002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.662020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.252140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.763469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.357370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.857117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.474749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:26.068141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.681782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.211447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.716617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.305210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.818145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.411590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.914023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.531109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:26.126463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.740812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.268347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.773938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.360794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.874330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.469669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.971206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.587254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:26.187348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.800270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.324568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.830842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.417966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.931332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.525204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.026595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.644211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:26.242980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.855757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.378409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.884640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.470816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.986302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.575522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.081966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.698175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:26.299608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.914340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.433350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.017074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.525142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.051991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.628437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.139661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.753229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:26.356278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:21.975924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:22.490551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.074769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:23.581890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.109071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:24.681785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.293745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:20:25.810531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-07-02T18:20:28.926150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
District CodeAll GradesTotal StudentsAfriAmerican-Black-%SecondaryHighTests Taken% Score 1-2% Score 3-5Primary
District Code1.000-0.205-0.295-0.016-0.160-0.184-0.0740.012-0.0530.000
All Grades-0.2051.0000.6440.6870.4120.9160.5230.4000.3770.694
Total Students-0.2950.6441.000-0.0080.0840.6720.6340.4560.4470.694
AfriAmerican-Black-%-0.0160.687-0.0081.0000.4280.5600.1370.1030.0580.181
Secondary-0.1600.4120.0840.4281.0000.1080.0930.0930.1380.694
High-0.1840.9160.6720.5600.1081.0000.5560.4050.3800.694
Tests Taken-0.0740.5230.6340.1370.0930.5561.0000.5730.5110.694
% Score 1-20.0120.4000.4560.1030.0930.4050.5731.0000.8450.074
% Score 3-5-0.0530.3770.4470.0580.1380.3800.5110.8451.0000.276
Primary0.0000.6940.6940.1810.6940.6940.6940.0740.2761.000

Missing values

2023-07-02T18:20:26.443282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-02T18:20:26.550836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

District Name_xDistrict CodeAll GradesTotal StudentsAfriAmerican-Black-%PrimarySecondaryHighDistrict Name_yTests Taken% Score 1-2% Score 3-5
0Abington100001.0111.00.9009010.00.01.0Abington1.00.00.0
1Academy Of the Pacific Rim Charter Public (District)412000014.0307.04.5602610.00.014.0Academy Of the Pacific Rim Charter Public (District)7.00.00.0
2Advanced Math and Science Academy Charter (District)430000010.021.047.6190480.03.07.0Advanced Math and Science Academy Charter (District)3.00.00.0
3Amherst-Pelham605000029.0108.026.8518520.00.029.0Amherst-Pelham2.00.00.0
4Andover900009.0139.06.4748200.00.09.0Andover2.00.00.0
5Arlington1000007.0204.03.4313730.00.07.0Arlington5.00.00.0
6Ashland1400002.080.02.5000000.00.02.0Ashland8.00.00.0
7Assabet Valley Regional Vocational Technical80100000.021.00.0000000.00.00.0Assabet Valley Regional Vocational Technical1.00.00.0
8Atlantis Charter (District)49100004.078.05.1282050.00.04.0Atlantis Charter (District)2.00.00.0
9Attleboro1600004.0474.00.8438820.00.04.0Attleboro4.00.00.0
District Name_xDistrict CodeAll GradesTotal StudentsAfriAmerican-Black-%PrimarySecondaryHighDistrict Name_yTests Taken% Score 1-2% Score 3-5
150West Bridgewater32300006.097.06.1855670.00.06.0West Bridgewater2.00.00.0
151West Springfield332000016.0157.010.1910830.013.03.0West Springfield1.00.00.0
152Westfield32500007.0104.06.7307690.06.01.0Westfield1.00.00.0
153Westwood33500001.058.01.7241380.00.01.0Westwood1.00.00.0
154Weymouth33600008.0458.01.7467250.00.08.0Weymouth3.00.00.0
155Whitman-Hanson78000006.0136.04.4117650.00.06.0Whitman-Hanson1.00.00.0
156Whittier Regional Vocational Technical88500000.039.00.0000000.00.00.0Whittier Regional Vocational Technical1.00.00.0
157Woburn347000031.0320.09.6875000.029.02.0Woburn4.00.00.0
158Worcester348000069.03841.01.7964070.00.069.0Worcester72.070.829.2
159State Totals02843.082946.03.42753133.0527.02283.0State Totals1140.070.629.4